Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations344
Missing cells363
Missing cells (%)6.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory210.2 KiB
Average record size in memory625.8 B

Variable types

Categorical8
Numeric7
Text1
Boolean1

Alerts

Region has constant value "Anvers" Constant
Stage has constant value "Adult, 1 Egg Stage" Constant
Body Mass (g) is highly overall correlated with Culmen Length (mm) and 3 other fieldsHigh correlation
Clutch Completion is highly overall correlated with CommentsHigh correlation
Comments is highly overall correlated with Clutch Completion and 1 other fieldsHigh correlation
Culmen Depth (mm) is highly overall correlated with Delta 15 N (o/oo) and 2 other fieldsHigh correlation
Culmen Length (mm) is highly overall correlated with Body Mass (g) and 2 other fieldsHigh correlation
Date Egg is highly overall correlated with Island and 3 other fieldsHigh correlation
Delta 13 C (o/oo) is highly overall correlated with Delta 15 N (o/oo) and 3 other fieldsHigh correlation
Delta 15 N (o/oo) is highly overall correlated with Body Mass (g) and 4 other fieldsHigh correlation
Flipper Length (mm) is highly overall correlated with Body Mass (g) and 5 other fieldsHigh correlation
Island is highly overall correlated with Date Egg and 3 other fieldsHigh correlation
Sample Number is highly overall correlated with Date Egg and 1 other fieldsHigh correlation
Species is highly overall correlated with Body Mass (g) and 8 other fieldsHigh correlation
studyName is highly overall correlated with Date Egg and 2 other fieldsHigh correlation
Clutch Completion is highly imbalanced (51.6%) Imbalance
Sex has 10 (2.9%) missing values Missing
Delta 15 N (o/oo) has 14 (4.1%) missing values Missing
Delta 13 C (o/oo) has 13 (3.8%) missing values Missing
Comments has 318 (92.4%) missing values Missing

Reproduction

Analysis started2025-02-09 16:42:22.186411
Analysis finished2025-02-09 16:42:32.490540
Duration10.3 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

studyName
Categorical

High correlation 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size18.9 KiB
PAL0910
120 
PAL0809
114 
PAL0708
110 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters2408
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPAL0708
2nd rowPAL0708
3rd rowPAL0708
4th rowPAL0708
5th rowPAL0708

Common Values

ValueCountFrequency (%)
PAL0910 120
34.9%
PAL0809 114
33.1%
PAL0708 110
32.0%

Length

2025-02-09T11:42:32.619764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:42:32.743853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
pal0910 120
34.9%
pal0809 114
33.1%
pal0708 110
32.0%

Most occurring characters

ValueCountFrequency (%)
0 688
28.6%
P 344
14.3%
A 344
14.3%
L 344
14.3%
9 234
 
9.7%
8 224
 
9.3%
1 120
 
5.0%
7 110
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 688
28.6%
P 344
14.3%
A 344
14.3%
L 344
14.3%
9 234
 
9.7%
8 224
 
9.3%
1 120
 
5.0%
7 110
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 688
28.6%
P 344
14.3%
A 344
14.3%
L 344
14.3%
9 234
 
9.7%
8 224
 
9.3%
1 120
 
5.0%
7 110
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 688
28.6%
P 344
14.3%
A 344
14.3%
L 344
14.3%
9 234
 
9.7%
8 224
 
9.3%
1 120
 
5.0%
7 110
 
4.6%

Sample Number
Real number (ℝ)

High correlation 

Distinct152
Distinct (%)44.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.151163
Minimum1
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-09T11:42:32.870215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.15
Q129
median58
Q395.25
95-th percentile134.85
Maximum152
Range151
Interquartile range (IQR)66.25

Descriptive statistics

Standard deviation40.430199
Coefficient of variation (CV)0.64021306
Kurtosis-0.9260372
Mean63.151163
Median Absolute Deviation (MAD)32
Skewness0.35140216
Sum21724
Variance1634.601
MonotonicityNot monotonic
2025-02-09T11:42:33.045908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3
 
0.9%
45 3
 
0.9%
52 3
 
0.9%
51 3
 
0.9%
50 3
 
0.9%
48 3
 
0.9%
47 3
 
0.9%
46 3
 
0.9%
44 3
 
0.9%
36 3
 
0.9%
Other values (142) 314
91.3%
ValueCountFrequency (%)
1 3
0.9%
2 3
0.9%
3 3
0.9%
4 3
0.9%
5 3
0.9%
6 3
0.9%
7 3
0.9%
8 3
0.9%
9 3
0.9%
10 3
0.9%
ValueCountFrequency (%)
152 1
0.3%
151 1
0.3%
150 1
0.3%
149 1
0.3%
148 1
0.3%
147 1
0.3%
146 1
0.3%
145 1
0.3%
144 1
0.3%
143 1
0.3%

Species
Categorical

High correlation 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
Adelie Penguin (Pygoscelis adeliae)
152 
Gentoo penguin (Pygoscelis papua)
124 
Chinstrap penguin (Pygoscelis antarctica)
68 

Length

Max length41
Median length35
Mean length35.465116
Min length33

Characters and Unicode

Total characters12200
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdelie Penguin (Pygoscelis adeliae)
2nd rowAdelie Penguin (Pygoscelis adeliae)
3rd rowAdelie Penguin (Pygoscelis adeliae)
4th rowAdelie Penguin (Pygoscelis adeliae)
5th rowAdelie Penguin (Pygoscelis adeliae)

Common Values

ValueCountFrequency (%)
Adelie Penguin (Pygoscelis adeliae) 152
44.2%
Gentoo penguin (Pygoscelis papua) 124
36.0%
Chinstrap penguin (Pygoscelis antarctica) 68
19.8%

Length

2025-02-09T11:42:33.212196image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:42:33.340203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
penguin 344
25.0%
pygoscelis 344
25.0%
adelie 152
11.0%
adeliae 152
11.0%
gentoo 124
 
9.0%
papua 124
 
9.0%
chinstrap 68
 
4.9%
antarctica 68
 
4.9%

Most occurring characters

ValueCountFrequency (%)
e 1420
 
11.6%
i 1128
 
9.2%
1032
 
8.5%
n 948
 
7.8%
a 824
 
6.8%
s 756
 
6.2%
g 688
 
5.6%
l 648
 
5.3%
o 592
 
4.9%
p 508
 
4.2%
Other values (13) 3656
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12200
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1420
 
11.6%
i 1128
 
9.2%
1032
 
8.5%
n 948
 
7.8%
a 824
 
6.8%
s 756
 
6.2%
g 688
 
5.6%
l 648
 
5.3%
o 592
 
4.9%
p 508
 
4.2%
Other values (13) 3656
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12200
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1420
 
11.6%
i 1128
 
9.2%
1032
 
8.5%
n 948
 
7.8%
a 824
 
6.8%
s 756
 
6.2%
g 688
 
5.6%
l 648
 
5.3%
o 592
 
4.9%
p 508
 
4.2%
Other values (13) 3656
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12200
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1420
 
11.6%
i 1128
 
9.2%
1032
 
8.5%
n 948
 
7.8%
a 824
 
6.8%
s 756
 
6.2%
g 688
 
5.6%
l 648
 
5.3%
o 592
 
4.9%
p 508
 
4.2%
Other values (13) 3656
30.0%

Region
Categorical

Constant 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
Anvers
344 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2064
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnvers
2nd rowAnvers
3rd rowAnvers
4th rowAnvers
5th rowAnvers

Common Values

ValueCountFrequency (%)
Anvers 344
100.0%

Length

2025-02-09T11:42:33.490290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:42:33.608223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
anvers 344
100.0%

Most occurring characters

ValueCountFrequency (%)
A 344
16.7%
n 344
16.7%
v 344
16.7%
e 344
16.7%
r 344
16.7%
s 344
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 344
16.7%
n 344
16.7%
v 344
16.7%
e 344
16.7%
r 344
16.7%
s 344
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 344
16.7%
n 344
16.7%
v 344
16.7%
e 344
16.7%
r 344
16.7%
s 344
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 344
16.7%
n 344
16.7%
v 344
16.7%
e 344
16.7%
r 344
16.7%
s 344
16.7%

Island
Categorical

High correlation 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size18.6 KiB
Biscoe
168 
Dream
124 
Torgersen
52 

Length

Max length9
Median length6
Mean length6.0930233
Min length5

Characters and Unicode

Total characters2096
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTorgersen
2nd rowTorgersen
3rd rowTorgersen
4th rowTorgersen
5th rowTorgersen

Common Values

ValueCountFrequency (%)
Biscoe 168
48.8%
Dream 124
36.0%
Torgersen 52
 
15.1%

Length

2025-02-09T11:42:33.727525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:42:33.852965image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
biscoe 168
48.8%
dream 124
36.0%
torgersen 52
 
15.1%

Most occurring characters

ValueCountFrequency (%)
e 396
18.9%
r 228
10.9%
s 220
10.5%
o 220
10.5%
B 168
8.0%
i 168
8.0%
c 168
8.0%
D 124
 
5.9%
a 124
 
5.9%
m 124
 
5.9%
Other values (3) 156
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 396
18.9%
r 228
10.9%
s 220
10.5%
o 220
10.5%
B 168
8.0%
i 168
8.0%
c 168
8.0%
D 124
 
5.9%
a 124
 
5.9%
m 124
 
5.9%
Other values (3) 156
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 396
18.9%
r 228
10.9%
s 220
10.5%
o 220
10.5%
B 168
8.0%
i 168
8.0%
c 168
8.0%
D 124
 
5.9%
a 124
 
5.9%
m 124
 
5.9%
Other values (3) 156
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 396
18.9%
r 228
10.9%
s 220
10.5%
o 220
10.5%
B 168
8.0%
i 168
8.0%
c 168
8.0%
D 124
 
5.9%
a 124
 
5.9%
m 124
 
5.9%
Other values (3) 156
 
7.4%

Stage
Categorical

Constant 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.6 KiB
Adult, 1 Egg Stage
344 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters6192
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdult, 1 Egg Stage
2nd rowAdult, 1 Egg Stage
3rd rowAdult, 1 Egg Stage
4th rowAdult, 1 Egg Stage
5th rowAdult, 1 Egg Stage

Common Values

ValueCountFrequency (%)
Adult, 1 Egg Stage 344
100.0%

Length

2025-02-09T11:42:33.988641image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:42:34.091962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
adult 344
25.0%
1 344
25.0%
egg 344
25.0%
stage 344
25.0%

Most occurring characters

ValueCountFrequency (%)
1032
16.7%
g 1032
16.7%
t 688
11.1%
A 344
 
5.6%
d 344
 
5.6%
u 344
 
5.6%
l 344
 
5.6%
, 344
 
5.6%
1 344
 
5.6%
E 344
 
5.6%
Other values (3) 1032
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6192
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1032
16.7%
g 1032
16.7%
t 688
11.1%
A 344
 
5.6%
d 344
 
5.6%
u 344
 
5.6%
l 344
 
5.6%
, 344
 
5.6%
1 344
 
5.6%
E 344
 
5.6%
Other values (3) 1032
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6192
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1032
16.7%
g 1032
16.7%
t 688
11.1%
A 344
 
5.6%
d 344
 
5.6%
u 344
 
5.6%
l 344
 
5.6%
, 344
 
5.6%
1 344
 
5.6%
E 344
 
5.6%
Other values (3) 1032
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6192
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1032
16.7%
g 1032
16.7%
t 688
11.1%
A 344
 
5.6%
d 344
 
5.6%
u 344
 
5.6%
l 344
 
5.6%
, 344
 
5.6%
1 344
 
5.6%
E 344
 
5.6%
Other values (3) 1032
16.7%
Distinct190
Distinct (%)55.2%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
2025-02-09T11:42:34.621377image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.9011628
Min length4

Characters and Unicode

Total characters1686
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique76 ?
Unique (%)22.1%

Sample

1st rowN1A1
2nd rowN1A2
3rd rowN2A1
4th rowN2A2
5th rowN3A1
ValueCountFrequency (%)
n61a2 3
 
0.9%
n36a1 3
 
0.9%
n38a1 3
 
0.9%
n38a2 3
 
0.9%
n39a1 3
 
0.9%
n39a2 3
 
0.9%
n61a1 3
 
0.9%
n21a2 3
 
0.9%
n69a1 3
 
0.9%
n18a2 3
 
0.9%
Other values (180) 314
91.3%
2025-02-09T11:42:35.327182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 344
20.4%
A 344
20.4%
2 260
15.4%
1 250
14.8%
3 82
 
4.9%
6 76
 
4.5%
4 70
 
4.2%
8 60
 
3.6%
5 60
 
3.6%
7 58
 
3.4%
Other values (2) 82
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 344
20.4%
A 344
20.4%
2 260
15.4%
1 250
14.8%
3 82
 
4.9%
6 76
 
4.5%
4 70
 
4.2%
8 60
 
3.6%
5 60
 
3.6%
7 58
 
3.4%
Other values (2) 82
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 344
20.4%
A 344
20.4%
2 260
15.4%
1 250
14.8%
3 82
 
4.9%
6 76
 
4.5%
4 70
 
4.2%
8 60
 
3.6%
5 60
 
3.6%
7 58
 
3.4%
Other values (2) 82
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 344
20.4%
A 344
20.4%
2 260
15.4%
1 250
14.8%
3 82
 
4.9%
6 76
 
4.5%
4 70
 
4.2%
8 60
 
3.6%
5 60
 
3.6%
7 58
 
3.4%
Other values (2) 82
 
4.9%

Clutch Completion
Boolean

High correlation  Imbalance 

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size476.0 B
True
308 
False
36 
ValueCountFrequency (%)
True 308
89.5%
False 36
 
10.5%
2025-02-09T11:42:35.485959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Date Egg
Categorical

High correlation 

Distinct50
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Memory size19.2 KiB
11/27/07
 
18
11/16/07
 
16
11/9/08
 
16
11/18/09
 
14
11/4/08
 
12
Other values (45)
268 

Length

Max length8
Median length8
Mean length7.7325581
Min length7

Characters and Unicode

Total characters2660
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11/11/07
2nd row11/11/07
3rd row11/16/07
4th row11/16/07
5th row11/16/07

Common Values

ValueCountFrequency (%)
11/27/07 18
 
5.2%
11/16/07 16
 
4.7%
11/9/08 16
 
4.7%
11/18/09 14
 
4.1%
11/4/08 12
 
3.5%
11/6/08 12
 
3.5%
11/13/08 12
 
3.5%
11/21/09 12
 
3.5%
11/14/08 10
 
2.9%
11/17/09 10
 
2.9%
Other values (40) 212
61.6%

Length

2025-02-09T11:42:35.619998image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11/27/07 18
 
5.2%
11/9/08 16
 
4.7%
11/16/07 16
 
4.7%
11/18/09 14
 
4.1%
11/4/08 12
 
3.5%
11/6/08 12
 
3.5%
11/13/08 12
 
3.5%
11/21/09 12
 
3.5%
11/16/09 10
 
2.9%
11/29/07 10
 
2.9%
Other values (40) 212
61.6%

Most occurring characters

ValueCountFrequency (%)
1 844
31.7%
/ 688
25.9%
0 364
13.7%
9 164
 
6.2%
7 154
 
5.8%
2 150
 
5.6%
8 146
 
5.5%
3 46
 
1.7%
6 42
 
1.6%
4 32
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 844
31.7%
/ 688
25.9%
0 364
13.7%
9 164
 
6.2%
7 154
 
5.8%
2 150
 
5.6%
8 146
 
5.5%
3 46
 
1.7%
6 42
 
1.6%
4 32
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 844
31.7%
/ 688
25.9%
0 364
13.7%
9 164
 
6.2%
7 154
 
5.8%
2 150
 
5.6%
8 146
 
5.5%
3 46
 
1.7%
6 42
 
1.6%
4 32
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 844
31.7%
/ 688
25.9%
0 364
13.7%
9 164
 
6.2%
7 154
 
5.8%
2 150
 
5.6%
8 146
 
5.5%
3 46
 
1.7%
6 42
 
1.6%
4 32
 
1.2%

Culmen Length (mm)
Real number (ℝ)

High correlation 

Distinct164
Distinct (%)48.0%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean43.92193
Minimum32.1
Maximum59.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-09T11:42:35.763418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum32.1
5-th percentile35.7
Q139.225
median44.45
Q348.5
95-th percentile51.995
Maximum59.6
Range27.5
Interquartile range (IQR)9.275

Descriptive statistics

Standard deviation5.4595837
Coefficient of variation (CV)0.124302
Kurtosis-0.87602697
Mean43.92193
Median Absolute Deviation (MAD)4.75
Skewness0.053118067
Sum15021.3
Variance29.807054
MonotonicityNot monotonic
2025-02-09T11:42:35.921393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.1 7
 
2.0%
45.2 6
 
1.7%
45.5 5
 
1.5%
39.6 5
 
1.5%
50.5 5
 
1.5%
46.5 5
 
1.5%
50 5
 
1.5%
37.8 5
 
1.5%
46.2 5
 
1.5%
46.4 4
 
1.2%
Other values (154) 290
84.3%
ValueCountFrequency (%)
32.1 1
0.3%
33.1 1
0.3%
33.5 1
0.3%
34 1
0.3%
34.1 1
0.3%
34.4 1
0.3%
34.5 1
0.3%
34.6 2
0.6%
35 2
0.6%
35.1 1
0.3%
ValueCountFrequency (%)
59.6 1
0.3%
58 1
0.3%
55.9 1
0.3%
55.8 1
0.3%
55.1 1
0.3%
54.3 1
0.3%
54.2 1
0.3%
53.5 1
0.3%
53.4 1
0.3%
52.8 1
0.3%

Culmen Depth (mm)
Real number (ℝ)

High correlation 

Distinct80
Distinct (%)23.4%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean17.15117
Minimum13.1
Maximum21.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-09T11:42:36.074156image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum13.1
5-th percentile13.9
Q115.6
median17.3
Q318.7
95-th percentile20
Maximum21.5
Range8.4
Interquartile range (IQR)3.1

Descriptive statistics

Standard deviation1.9747932
Coefficient of variation (CV)0.11514044
Kurtosis-0.90686609
Mean17.15117
Median Absolute Deviation (MAD)1.5
Skewness-0.14346463
Sum5865.7
Variance3.899808
MonotonicityNot monotonic
2025-02-09T11:42:36.215239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 12
 
3.5%
15 10
 
2.9%
18.6 10
 
2.9%
17.9 10
 
2.9%
18.5 10
 
2.9%
17.3 9
 
2.6%
18.9 9
 
2.6%
19 9
 
2.6%
17.8 9
 
2.6%
18.1 9
 
2.6%
Other values (70) 245
71.2%
ValueCountFrequency (%)
13.1 1
 
0.3%
13.2 1
 
0.3%
13.3 1
 
0.3%
13.4 1
 
0.3%
13.5 2
 
0.6%
13.6 1
 
0.3%
13.7 6
1.7%
13.8 4
1.2%
13.9 4
1.2%
14 2
 
0.6%
ValueCountFrequency (%)
21.5 1
 
0.3%
21.2 2
0.6%
21.1 3
0.9%
20.8 1
 
0.3%
20.7 3
0.9%
20.6 1
 
0.3%
20.5 1
 
0.3%
20.3 3
0.9%
20.2 1
 
0.3%
20.1 1
 
0.3%

Flipper Length (mm)
Real number (ℝ)

High correlation 

Distinct55
Distinct (%)16.1%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean200.9152
Minimum172
Maximum231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-09T11:42:36.350862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum172
5-th percentile181
Q1190
median197
Q3213
95-th percentile225
Maximum231
Range59
Interquartile range (IQR)23

Descriptive statistics

Standard deviation14.061714
Coefficient of variation (CV)0.0699883
Kurtosis-0.98427289
Mean200.9152
Median Absolute Deviation (MAD)11
Skewness0.34568183
Sum68713
Variance197.73179
MonotonicityNot monotonic
2025-02-09T11:42:36.510212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190 22
 
6.4%
195 17
 
4.9%
187 16
 
4.7%
193 15
 
4.4%
210 14
 
4.1%
191 13
 
3.8%
215 12
 
3.5%
196 10
 
2.9%
197 10
 
2.9%
185 9
 
2.6%
Other values (45) 204
59.3%
ValueCountFrequency (%)
172 1
 
0.3%
174 1
 
0.3%
176 1
 
0.3%
178 4
1.2%
179 1
 
0.3%
180 5
1.5%
181 7
2.0%
182 3
0.9%
183 2
 
0.6%
184 7
2.0%
ValueCountFrequency (%)
231 1
 
0.3%
230 7
2.0%
229 2
 
0.6%
228 4
1.2%
226 1
 
0.3%
225 4
1.2%
224 3
0.9%
223 2
 
0.6%
222 6
1.7%
221 5
1.5%

Body Mass (g)
Real number (ℝ)

High correlation 

Distinct94
Distinct (%)27.5%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean4201.7544
Minimum2700
Maximum6300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-09T11:42:36.817423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2700
5-th percentile3150
Q13550
median4050
Q34750
95-th percentile5650
Maximum6300
Range3600
Interquartile range (IQR)1200

Descriptive statistics

Standard deviation801.95454
Coefficient of variation (CV)0.19086183
Kurtosis-0.71922187
Mean4201.7544
Median Absolute Deviation (MAD)600
Skewness0.47032933
Sum1437000
Variance643131.08
MonotonicityNot monotonic
2025-02-09T11:42:36.993586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3800 12
 
3.5%
3700 11
 
3.2%
3900 10
 
2.9%
3950 10
 
2.9%
3550 9
 
2.6%
4400 8
 
2.3%
4300 8
 
2.3%
3450 8
 
2.3%
3400 8
 
2.3%
3600 7
 
2.0%
Other values (84) 251
73.0%
ValueCountFrequency (%)
2700 1
 
0.3%
2850 2
0.6%
2900 4
1.2%
2925 1
 
0.3%
2975 1
 
0.3%
3000 2
0.6%
3050 4
1.2%
3075 1
 
0.3%
3100 1
 
0.3%
3150 4
1.2%
ValueCountFrequency (%)
6300 1
 
0.3%
6050 1
 
0.3%
6000 2
 
0.6%
5950 2
 
0.6%
5850 3
0.9%
5800 2
 
0.6%
5750 1
 
0.3%
5700 5
1.5%
5650 3
0.9%
5600 2
 
0.6%

Sex
Categorical

Missing 

Distinct3
Distinct (%)0.9%
Missing10
Missing (%)2.9%
Memory size18.3 KiB
MALE
168 
FEMALE
165 
.
 
1

Length

Max length6
Median length4
Mean length4.9790419
Min length1

Characters and Unicode

Total characters1663
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowMALE
2nd rowFEMALE
3rd rowFEMALE
4th rowFEMALE
5th rowMALE

Common Values

ValueCountFrequency (%)
MALE 168
48.8%
FEMALE 165
48.0%
. 1
 
0.3%
(Missing) 10
 
2.9%

Length

2025-02-09T11:42:37.150231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:42:37.260096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
male 168
50.3%
female 165
49.4%
1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
E 498
29.9%
M 333
20.0%
A 333
20.0%
L 333
20.0%
F 165
 
9.9%
. 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 498
29.9%
M 333
20.0%
A 333
20.0%
L 333
20.0%
F 165
 
9.9%
. 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 498
29.9%
M 333
20.0%
A 333
20.0%
L 333
20.0%
F 165
 
9.9%
. 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 498
29.9%
M 333
20.0%
A 333
20.0%
L 333
20.0%
F 165
 
9.9%
. 1
 
0.1%

Delta 15 N (o/oo)
Real number (ℝ)

High correlation  Missing 

Distinct330
Distinct (%)100.0%
Missing14
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean8.7333817
Minimum7.6322
Maximum10.02544
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-02-09T11:42:37.404622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum7.6322
5-th percentile7.896758
Q18.29989
median8.652405
Q39.1721225
95-th percentile9.6894235
Maximum10.02544
Range2.39324
Interquartile range (IQR)0.8722325

Descriptive statistics

Standard deviation0.55177034
Coefficient of variation (CV)0.06317946
Kurtosis-0.74807738
Mean8.7333817
Median Absolute Deviation (MAD)0.408565
Skewness0.23898106
Sum2882.016
Variance0.3044505
MonotonicityNot monotonic
2025-02-09T11:42:37.556326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.84057 1
 
0.3%
7.63884 1
 
0.3%
8.07137 1
 
0.3%
7.79958 1
 
0.3%
7.8208 1
 
0.3%
7.77672 1
 
0.3%
8.10417 1
 
0.3%
8.19579 1
 
0.3%
8.1631 1
 
0.3%
8.13643 1
 
0.3%
Other values (320) 320
93.0%
(Missing) 14
 
4.1%
ValueCountFrequency (%)
7.6322 1
0.3%
7.63452 1
0.3%
7.63884 1
0.3%
7.68528 1
0.3%
7.6887 1
0.3%
7.69778 1
0.3%
7.76843 1
0.3%
7.77672 1
0.3%
7.79958 1
0.3%
7.8208 1
0.3%
ValueCountFrequency (%)
10.02544 1
0.3%
10.02372 1
0.3%
10.02019 1
0.3%
9.98044 1
0.3%
9.93727 1
0.3%
9.88809 1
0.3%
9.8059 1
0.3%
9.80589 1
0.3%
9.79532 1
0.3%
9.77528 1
0.3%

Delta 13 C (o/oo)
Real number (ℝ)

High correlation  Missing 

Distinct331
Distinct (%)100.0%
Missing13
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean-25.686292
Minimum-27.01854
Maximum-23.78767
Zeros0
Zeros (%)0.0%
Negative331
Negative (%)96.2%
Memory size2.8 KiB
2025-02-09T11:42:37.713401image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-27.01854
5-th percentile-26.790055
Q1-26.320305
median-25.83352
Q3-25.06205
95-th percentile-24.36166
Maximum-23.78767
Range3.23087
Interquartile range (IQR)1.258255

Descriptive statistics

Standard deviation0.79396121
Coefficient of variation (CV)-0.03090992
Kurtosis-1.0304631
Mean-25.686292
Median Absolute Deviation (MAD)0.60688
Skewness0.33778125
Sum-8502.1625
Variance0.6303744
MonotonicityNot monotonic
2025-02-09T11:42:37.887540image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-25.37899 1
 
0.3%
-25.52627 1
 
0.3%
-25.52473 1
 
0.3%
-25.62618 1
 
0.3%
-25.48025 1
 
0.3%
-25.4168 1
 
0.3%
-25.50562 1
 
0.3%
-25.3933 1
 
0.3%
-25.38017 1
 
0.3%
-25.32176 1
 
0.3%
Other values (321) 321
93.3%
(Missing) 13
 
3.8%
ValueCountFrequency (%)
-27.01854 1
0.3%
-26.9547 1
0.3%
-26.89644 1
0.3%
-26.86485 1
0.3%
-26.86352 1
0.3%
-26.86127 1
0.3%
-26.84506 1
0.3%
-26.84415 1
0.3%
-26.84374 1
0.3%
-26.84272 1
0.3%
ValueCountFrequency (%)
-23.78767 1
0.3%
-23.89017 1
0.3%
-23.90309 1
0.3%
-24.10255 1
0.3%
-24.16566 1
0.3%
-24.17282 1
0.3%
-24.23592 1
0.3%
-24.25255 1
0.3%
-24.26375 1
0.3%
-24.29229 1
0.3%

Comments
Categorical

High correlation  Missing 

Distinct7
Distinct (%)26.9%
Missing318
Missing (%)92.4%
Memory size19.7 KiB
Nest never observed with full clutch.
13 
Not enough blood for isotopes.
No blood sample obtained.
No blood sample obtained for sexing.
Adult not sampled.
 
1
Other values (2)

Length

Max length68
Median length65
Mean length35.807692
Min length18

Characters and Unicode

Total characters931
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)11.5%

Sample

1st rowNot enough blood for isotopes.
2nd rowAdult not sampled.
3rd rowNest never observed with full clutch.
4th rowNest never observed with full clutch.
5th rowNo blood sample obtained.

Common Values

ValueCountFrequency (%)
Nest never observed with full clutch. 13
 
3.8%
Not enough blood for isotopes. 6
 
1.7%
No blood sample obtained. 2
 
0.6%
No blood sample obtained for sexing. 2
 
0.6%
Adult not sampled. 1
 
0.3%
Nest never observed with full clutch. Not enough blood for isotopes. 1
 
0.3%
Sexing primers did not amplify. Not enough blood for isotopes. 1
 
0.3%
(Missing) 318
92.4%

Length

2025-02-09T11:42:38.038296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-09T11:42:38.163291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
nest 14
9.2%
observed 14
9.2%
with 14
9.2%
full 14
9.2%
clutch 14
9.2%
never 14
9.2%
blood 12
7.9%
for 10
 
6.6%
not 10
 
6.6%
isotopes 8
 
5.3%
Other values (10) 28
18.4%

Most occurring characters

ValueCountFrequency (%)
126
13.5%
e 99
 
10.6%
o 90
 
9.7%
t 65
 
7.0%
l 61
 
6.6%
s 52
 
5.6%
r 40
 
4.3%
u 37
 
4.0%
h 36
 
3.9%
d 34
 
3.7%
Other values (17) 291
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 931
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
126
13.5%
e 99
 
10.6%
o 90
 
9.7%
t 65
 
7.0%
l 61
 
6.6%
s 52
 
5.6%
r 40
 
4.3%
u 37
 
4.0%
h 36
 
3.9%
d 34
 
3.7%
Other values (17) 291
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 931
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
126
13.5%
e 99
 
10.6%
o 90
 
9.7%
t 65
 
7.0%
l 61
 
6.6%
s 52
 
5.6%
r 40
 
4.3%
u 37
 
4.0%
h 36
 
3.9%
d 34
 
3.7%
Other values (17) 291
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 931
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
126
13.5%
e 99
 
10.6%
o 90
 
9.7%
t 65
 
7.0%
l 61
 
6.6%
s 52
 
5.6%
r 40
 
4.3%
u 37
 
4.0%
h 36
 
3.9%
d 34
 
3.7%
Other values (17) 291
31.3%

Interactions

2025-02-09T11:42:30.544267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:23.655837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:25.196286image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:26.719662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:27.712630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:28.663610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:29.601411image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:30.680701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:23.903195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:25.498116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:26.919862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:27.857661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:28.792914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:29.749421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:30.820141image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:24.095497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:25.700269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:27.057450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:27.992085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:28.914874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:29.871396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:30.933935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:24.289292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:25.900054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:27.166412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:28.122436image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:29.045858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:29.993722image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:31.096468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:24.546083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:26.107694image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:27.308368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:28.260138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:29.201706image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:30.132695image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:31.229853image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:24.745726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:26.309878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:27.429504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:28.401036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:29.336056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:30.292955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:31.384777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:24.977779image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:26.511423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:27.570882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:28.524627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:29.475661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-02-09T11:42:30.431175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-02-09T11:42:38.320009image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Body Mass (g)Clutch CompletionCommentsCulmen Depth (mm)Culmen Length (mm)Date EggDelta 13 C (o/oo)Delta 15 N (o/oo)Flipper Length (mm)IslandSample NumberSexSpeciesstudyName
Body Mass (g)1.0000.0540.336-0.4320.5840.027-0.387-0.5540.8400.4560.0090.4210.6050.000
Clutch Completion0.0541.0000.8900.2000.0000.3480.0960.1620.0710.1230.1510.0000.1510.051
Comments0.3360.8901.0000.1660.2420.1730.0000.0000.0000.0000.0000.0001.0000.000
Culmen Depth (mm)-0.4320.2000.1661.000-0.2220.1820.4310.616-0.5230.484-0.0500.4140.6350.107
Culmen Length (mm)0.5840.0000.242-0.2221.0000.2380.150-0.0940.6730.324-0.2130.3620.6500.117
Date Egg0.0270.3480.1730.1820.2381.0000.4570.3500.2660.6840.5620.0000.7610.929
Delta 13 C (o/oo)-0.3870.0960.0000.4310.1500.4571.0000.545-0.3560.502-0.4950.0000.6900.633
Delta 15 N (o/oo)-0.5540.1620.0000.616-0.0940.3500.5451.000-0.5000.4730.0090.0710.6100.304
Flipper Length (mm)0.8400.0710.000-0.5230.6730.266-0.356-0.5001.0000.5010.0600.3140.7010.219
Island0.4560.1230.0000.4840.3240.6840.5020.4730.5011.0000.4160.0000.6570.058
Sample Number0.0090.1510.000-0.050-0.2130.562-0.4950.0090.0600.4161.0000.0000.2960.750
Sex0.4210.0000.0000.4140.3620.0000.0000.0710.3140.0000.0001.0000.0000.000
Species0.6050.1511.0000.6350.6500.7610.6900.6100.7010.6570.2960.0001.0000.000
studyName0.0000.0510.0000.1070.1170.9290.6330.3040.2190.0580.7500.0000.0001.000

Missing values

2025-02-09T11:42:31.717292image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-09T11:42:32.046253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-09T11:42:32.337020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

studyNameSample NumberSpeciesRegionIslandStageIndividual IDClutch CompletionDate EggCulmen Length (mm)Culmen Depth (mm)Flipper Length (mm)Body Mass (g)SexDelta 15 N (o/oo)Delta 13 C (o/oo)Comments
0PAL07081Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN1A1Yes11/11/0739.118.7181.03750.0MALENaNNaNNot enough blood for isotopes.
1PAL07082Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN1A2Yes11/11/0739.517.4186.03800.0FEMALE8.94956-24.69454NaN
2PAL07083Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN2A1Yes11/16/0740.318.0195.03250.0FEMALE8.36821-25.33302NaN
3PAL07084Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN2A2Yes11/16/07NaNNaNNaNNaNNaNNaNNaNAdult not sampled.
4PAL07085Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN3A1Yes11/16/0736.719.3193.03450.0FEMALE8.76651-25.32426NaN
5PAL07086Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN3A2Yes11/16/0739.320.6190.03650.0MALE8.66496-25.29805NaN
6PAL07087Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN4A1No11/15/0738.917.8181.03625.0FEMALE9.18718-25.21799Nest never observed with full clutch.
7PAL07088Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN4A2No11/15/0739.219.6195.04675.0MALE9.46060-24.89958Nest never observed with full clutch.
8PAL07089Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN5A1Yes11/9/0734.118.1193.03475.0NaNNaNNaNNo blood sample obtained.
9PAL070810Adelie Penguin (Pygoscelis adeliae)AnversTorgersenAdult, 1 Egg StageN5A2Yes11/9/0742.020.2190.04250.0NaN9.13362-25.09368No blood sample obtained for sexing.
studyNameSample NumberSpeciesRegionIslandStageIndividual IDClutch CompletionDate EggCulmen Length (mm)Culmen Depth (mm)Flipper Length (mm)Body Mass (g)SexDelta 15 N (o/oo)Delta 13 C (o/oo)Comments
334PAL0910115Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN35A1Yes11/25/0946.214.1217.04375.0FEMALE8.30231-25.96013NaN
335PAL0910116Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN35A2Yes11/25/0955.116.0230.05850.0MALE8.08354-26.18161NaN
336PAL0910117Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN36A1Yes12/1/0944.515.7217.04875.0.8.04111-26.18444NaN
337PAL0910118Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN36A2Yes12/1/0948.816.2222.06000.0MALE8.33825-25.88547NaN
338PAL0910119Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN38A1No12/1/0947.213.7214.04925.0FEMALE7.99184-26.20538NaN
339PAL0910120Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN38A2No12/1/09NaNNaNNaNNaNNaNNaNNaNNaN
340PAL0910121Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN39A1Yes11/22/0946.814.3215.04850.0FEMALE8.41151-26.13832NaN
341PAL0910122Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN39A2Yes11/22/0950.415.7222.05750.0MALE8.30166-26.04117NaN
342PAL0910123Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN43A1Yes11/22/0945.214.8212.05200.0FEMALE8.24246-26.11969NaN
343PAL0910124Gentoo penguin (Pygoscelis papua)AnversBiscoeAdult, 1 Egg StageN43A2Yes11/22/0949.916.1213.05400.0MALE8.36390-26.15531NaN